title: Single-trial discrimination of EEG signals for stroke patients: a general multi-way analysis.
abstract: It has been demonstrated that Brain-Computer Interface (BCI), combined with Functional Electrical Stimulation (FES), is an effective and efficient way for post-stroke patients to restore motor function. However, traditional feature extraction methods, such as Common Spatial Pattern (CSP), do not work well for post-stroke patients' EEG data due to its irregular patterns. In this study, we introduce a novel tensorbased feature extraction algorithm, which takes both spatial-spectral-temporal features of EEG data into consideration. EEG data recorded from post-stroke patients is used for simulation experiments to assess the effectiveness of the proposed algorithm. The results show that the the proposed algorithm outperforms some traditional algorithms.
authors.forename: Ye
authors.initials: Y
authors.lastname: Liu
authors.forename: Mingfen
authors.initials: M
authors.lastname: Li
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.forename: Junhua
authors.initials: J
authors.lastname: Li
authors.forename: Jie
authors.initials: J
authors.lastname: Jia
authors.forename: Yi
authors.initials: Y
authors.lastname: Wu
authors.forename: Jianting
authors.initials: J
authors.lastname: Cao
authors.forename: Liqing
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000465:Algorithms; D004569:Electroencephalography; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000503:physiopathology*